ReviewJournal of clinical and experimental dentistry2025
Assessment of occult cervical metastases in oral cavity squamous cell carcinoma: Findings from a tertiary-level hospital study.
Review in Journal of clinical and experimental dentistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
6 authors.
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Abstract
Background: Cervical lymph node metastasis is the key prognostic factor in head and neck squamous cell carcinoma (HNSCC), particularly in oral cavity squamous cell carcinoma (OCSCC). Detecting occult metastases in clinically node-negative (cN0) necks remains challenging, as CT and PET-CT exhibit limited sensitivity and specificity. This study aimed to assess the incidence of occult cervical metastases in OCSCC patients, evaluate imaging accuracy, and identify associated histopathological factors. Material and Methods: We retrospectively analyzed 43 OCSCC patients undergoing elective neck dissection (END) from 2017 - 2024 at a tertiary hospital. Data on clinical staging, imaging findings, histopathological characteristics (depth of invasion [DOI], lymphovascular invasion [LVI]) and tumor location, were collected. Statistical tests (t-tests, chi-square) were performed using R v3.6.1. Results: Occult cervical metastases occurred in 23.26% of patients (10/43). CT sensitivity was 70.27%, specificity 82.86%; PET-CT showed 81.25% sensitivity and 71.43% specificity. Patients with occult metastases had a higher mean DOI (13.13 ± 10.39 mm) compared to those without (8.1 ± 5.85 mm), though this difference was not statistically significant (p=0.172). LVI was absent in the metastatic group but present in 9.09% of non-metastatic cases. Tumor location did not significantly correlate with occult metastases (p=0.801). Conclusions: Our findings confirm DOI as a crucial predictor of occult metastases in OCSCC, reinforcing the importance of END in high-risk patients. While PET-CT showed higher sensitivity than CT, both imaging modalities had limitations in detecting micrometastases. Further studies with larger sample sizes are needed to validate these findings and explore additional predictive factors, potentially integrating machine learning models for improved risk stratification.
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